test_trainer

This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 1.4916
• Accuracy: 0.659

Model description

This model is a fine-tuned model based on Sentiment Analysis or Text Classification for reviews based on the new 'Threads' app. The reviews dataset can be found on Kaggle.

Intended uses & limitations

Basically it converts the review text into rating points from 1-5(1 being a very bad review and 5 being a very good review)

Training and evaluation data

'Reviews' dataset(Thread) from Kaggle.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	250	1.0560	0.6895
0.5502	2.0	500	1.3548	0.6595
0.5502	3.0	750	1.4916	0.659

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.4
• Tokenizers 0.13.3